Causation, Prediction, and Search

Multitask Learning

Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636 · see citing works

Network analysis: An integrative approach to the structure of psychopathology

Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359 · see citing works

Causal inference in statistics: An overview

Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336 · see citing works

Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology

Hernan, M. A. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.2.176 · Cited by 1,445 · see citing works

Network analysis of multivariate data in psychological science

Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00055-w · Cited by 1,371 · see citing works

Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable

Elwert, F., & Winship, C. (2014) · Annual Review of Sociology · doi:10.1146/annurev-soc-071913-043455 · Cited by 1,168 · see citing works

Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data

Rohrer, J. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917745629 · Cited by 1,002 · see citing works

Artificial Intelligence and Black‐Box Medical Decisions: <i>Accuracy versus Explainability</i>

London, A. J. (2019) · Hastings Center Report · doi:10.1002/hast.973 · Cited by 801 · see citing works

An Introduction to Causal Inference

Pearl, J. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1203 · Cited by 755 · see citing works

Deconstructing the construct: a network perspective on psychological phenomena

Schmittmann, V. D., Cramer, A. O. J., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2011.02.007 · Cited by 740 · see citing works

The seven tools of causal inference, with reflections on machine learning

Pearl, J. (2019) · Communications of the ACM · doi:10.1145/3241036 · Cited by 659 · see citing works

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103473 · Cited by 652 · see citing works

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569 · see citing works

Simpson's paradox in psychological science: a practical guide

Kievit, R. A., Frankenhuis, W. E., Waldorp, L. J., & Borsboom, D. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00513 · Cited by 542 · see citing works

Six problems for causal inference from fMRI

Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R. A., & Glymour, C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.065 · Cited by 337 · see citing works

Causal discovery and inference: concepts and recent methodological advances

Spirtes, P., & Zhang, K. (2016) · Applied Informatics · doi:10.1186/s40535-016-0018-x · Cited by 304 · see citing works

Longitudinal Study of Postconcussion Syndrome: Not Everyone Recovers

Hiploylee, C., Dufort, P. A., Davis, H. S., Wennberg, R. A., Tartaglia, M. C., Mikulis, D., Hazrati, L.-N., & Tator, C. H. (2017) · Journal of Neurotrauma · doi:10.1089/neu.2016.4677 · Cited by 293 · see citing works

Network discovery with DCM

Friston, K. J., Li, B., Daunizeau, J., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.039 · Cited by 283 · see citing works

Inductive biases for deep learning of higher-level cognition

Goyal, A., & Bengio, Y. (2022) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2021.0068 · Cited by 300 · see citing works

Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals

Dahabreh, I. J., & Bibbins-Domingo, K. (2024) · JAMA · doi:10.1001/jama.2024.7741 · Cited by 242 · see citing works

Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis

Baumgartner, M., & Thiem, A. (2020) · Sociological Methods &amp; Research · doi:10.1177/0049124117701487 · Cited by 193 · see citing works

CloudRanger: Root Cause Identification for Cloud Native Systems

Wang, P., Xu, J., Ma, M., Lin, W., Pan, D., Wang, Y., & Chen, P. (2018) · 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) · doi:10.1109/ccgrid.2018.00076 · Cited by 181 · see citing works

Using genetic markers to orient the edges in quantitative trait networks: The NEO software

Aten, J. E., Fuller, T. F., Lusis, A. J., & Horvath, S. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-34 · Cited by 147 · see citing works

Methods for causal inference from gene perturbation experiments and validation

Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., & Bühlmann, P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510493113 · Cited by 140 · see citing works

The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"

Ward, A. C. (2009) · Epidemiologic Perspectives &amp; Innovations · doi:10.1186/1742-5573-6-2 · Cited by 120 · see citing works

Learning Functional Causal Models with Generative Neural Networks

Goudet, O., Kalainathan, D., Caillou, P., Guyon, I., Lopez-Paz, D., & Sebag, M. (2018) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-319-98131-4_3 · Cited by 123 · see citing works

Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes

Chaibub Neto, E., Keller, M. P., Attie, A. D., & Yandell, B. S. (2010) · The Annals of Applied Statistics · doi:10.1214/09-aoas288 · Cited by 110 · see citing works

First principles in the life sciences: the free-energy principle, organicism, and mechanism

Colombo, M., & Wright, C. (2021) · Synthese · doi:10.1007/s11229-018-01932-w · Cited by 101 · see citing works

AI, Opacity, and Personal Autonomy

Vaassen, B. (2022) · Philosophy &amp; Technology · doi:10.1007/s13347-022-00577-5 · Cited by 94 · see citing works

A generalized back-door criterion

Maathuis, M. H., & Colombo, D. (2015) · The Annals of Statistics · doi:10.1214/14-aos1295 · Cited by 88 · see citing works

A Case for Cases: Comparative Narratives in Sociological Explanation

Abell, P. (2009) · Sociological Methods &amp; Research · doi:10.1177/0049124109339372 · Cited by 79 · see citing works

Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?

London, A. J. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100622 · Cited by 76 · see citing works

On User-Centric Modular QoE Prediction for VoIP Based on Machine-Learning Algorithms

Charonyktakis, P., Plakia, M., Tsamardinos, I., & Papadopouli, M. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2015.2461216 · Cited by 75 · see citing works

MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems

Wu, L., Tordsson, J., Bogatinovski, J., Elmroth, E., & Kao, O. (2021) · 2021 IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence) · doi:10.1109/cloudintelligence52565.2021.00015 · Cited by 72 · see citing works

The impact of service level on the acceptance of application service oriented medical records

Liu, L., & Ma, Q. (2005) · Information &amp; Management · doi:10.1016/j.im.2004.12.004 · Cited by 59 · see citing works

Check Your Truth Conditions! Clarifying the Relationship between Theories of Causation and Social Science Methods for Causal Inference

Rohlfing, I., & Zuber, C. I. (2021) · Sociological Methods &amp; Research · doi:10.1177/0049124119826156 · Cited by 53 · see citing works

MS-Rank: Multi-Metric and Self-Adaptive Root Cause Diagnosis for Microservice Applications

Ma, M., Lin, W., Pan, D., & Wang, P. (2019) · 2019 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2019.00022 · Cited by 58 · see citing works

CausPref: Causal Preference Learning for Out-of-Distribution Recommendation

He, Y., Wang, Z., Cui, P., Zou, H., Zhang, Y., Cui, Q., & Jiang, Y. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3511969 · Cited by 50 · see citing works

Anti-discrimination learning: a causal modeling-based framework

Zhang, L., & Wu, X. (2017) · International Journal of Data Science and Analytics · doi:10.1007/s41060-017-0058-x · Cited by 48 · see citing works

ServiceRank: Root Cause Identification of Anomaly in Large-Scale Microservice Architectures

Ma, M., Lin, W., Pan, D., & Wang, P. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3083671 · Cited by 46 · see citing works

Neutral monism reconsidered

Banks, E. C. (2010) · Philosophical Psychology · doi:10.1080/09515081003690418 · Cited by 44 · see citing works

Trust and Trustworthiness in AI

Durán, J. M., & Pozzi, G. (2025) · Philosophy &amp; Technology · doi:10.1007/s13347-025-00843-2 · Cited by 47 · see citing works

Self-Adaptive Root Cause Diagnosis for Large-Scale Microservice Architecture

Ma, M., Lin, W., Pan, D., & Wang, P. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2020.2993251 · Cited by 37 · see citing works

Causal Structure Learning: A Combinatorial Perspective

Squires, C., & Uhler, C. (2023) · Foundations of Computational Mathematics · doi:10.1007/s10208-022-09581-9 · Cited by 31 · see citing works

FacGraph: Frequent Anomaly Correlation Graph Mining for Root Cause Diagnose in Micro-Service Architecture

Lin, W., Ma, M., Pan, D., & Wang, P. (2018) · 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) · doi:10.1109/pccc.2018.8711092 · Cited by 29 · see citing works

A discrimination-free premium under a causal framework

Araiza Iturria, C. A., Hardy, M., & Marriott, P. (2024) · North American Actuarial Journal · doi:10.1080/10920277.2023.2291524 · Cited by 20 · see citing works

Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing

Xing, S., Wang, Y., & Liu, W. (2025) · Sensors · doi:10.3390/s25113396 · Cited by 23 · see citing works

A Discrimination-Free Premium Under a Causal Framework

Araiza Iturria, C. A., Hardy, M., & Marriott, P. (2022) · doi:10.2139/ssrn.4079068 · Cited by 20 · see citing works

Root Cause and Liability Analysis in the Microservices Architecture for Edge IoT Services

Kalinagac, O., Soussi, W., Anser, Y., Gaber, C., & Gür, G. (2023) · ICC 2023 - IEEE International Conference on Communications · doi:10.1109/icc45041.2023.10279721 · Cited by 6 · see citing works

Robust agents learn causal world models

Richens, J., & Everitt, T. (2024) · arXiv · doi:10.48550/arxiv.2402.10877 · Cited by 5 · see citing works

The Precautionary Principle Meets the Hill Criteria of Causation

Steel, D., & Yu, J. (2019) · Ethics, Policy &amp; Environment · doi:10.1080/21550085.2019.1581420 · Cited by 4 · see citing works

PyRCA: A Library for Metric-based Root Cause Analysis

Liu, C., Yang, W., Mittal, H., Singh, M., Sahoo, D., & Hoi, S. C. H. (2023) · arXiv · doi:10.48550/arxiv.2306.11417 · Cited by 4 · see citing works

Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs

Hakizimana, G., & Ledezma Espino, A. (2025) · Algorithms · doi:10.3390/a18060306 · Cited by 2 · see citing works

Diagnosing and Mitigating Sycophancy and Skepticism in LLM Causal Judgment

Chang, E. Y. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.427 · Cited by 2 · see citing works

Confounding Equivalence in Causal Inference

Pearl, J., & Paz, A. (2014) · Journal of Causal Inference · doi:10.1515/jci-2013-0020 · Cited by 2 · see citing works

From Correlation to Intervention: A Survey on Log-Based Causal Analysis for Security and AIOps

Zhu, Y., Li, X., Suo, J., Yin, Y., & Huang, C. (2026) · 2026 International Conference on AI in Education Technology and Applications (AIETA) · doi:10.1109/aieta69357.2026.11508946